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. 2022 May 31:2022:6499217.
doi: 10.1155/2022/6499217. eCollection 2022.

SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load

Martin Skarzynski  1 , Erin M McAuley  1 , Ezekiel J Maier  1 , Anthony C Fries  2 , Jameson D Voss  3 , Richard R Chapleau  2
Affiliations

SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load

Martin Skarzynski et al. Glob Health Epidemiol Genom. .

Abstract

The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be "severe" (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the "mild" category (severity probability <0.5) had an average Ct of 20.4 (P=0.0017). We also found a nontrivial correlation between predicted severity probability and cycle threshold (r = -0.199). Finally, when divided into severity probability quartiles, the group most likely to experience severe illness (≥75% probability) had a Ct of 16.6 (n = 10), whereas the group least likely to experience severe illness (<25% probability) had a Ct of 21.4 (n = 350) (P=0.0045). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to clinical diagnostic tests and that relative severity may be inferred from diagnostic values.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Process flowchart for orthogonal validation of a severity prediction algorithm. Sequences were downloaded from GISAID, processed through reference alignment and variant calling, predicted based upon variants identified by Voss et al., and compared to observed PCR threshold data by the t-test and correlations. The matrix used for predictions was 716 samples with 662 variants overlapping the Voss et al. variant list (all other variants were assigned 0 for Python predictions).
Figure 2
Figure 2
PCR Ct values for viral specimens predicted to be from patients with mild (open box, n = 636) or severe (dotted box, n = 80) outcomes. ∗∗P=0.0017.
Figure 3
Figure 3
PCR Ct values for viral specimens least likely (probability < 25%, open box, n = 350) or most likely (probability ≥ 75%, dotted box, n = 10) outcomes. ∗∗P=0.0045.
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